All watermarks are shared into shadow images using VCS(Visual Cryptography Scheme). Only one specific shadow (as a new watermark) is embedded into the host signal and the other ones are distributed to every user in ea...All watermarks are shared into shadow images using VCS(Visual Cryptography Scheme). Only one specific shadow (as a new watermark) is embedded into the host signal and the other ones are distributed to every user in each group as a key. In the watermarking extraction procedure, users in different group can obtain different watermark by combining their shadows with the extracted one from the watermarked signal. Analysis and experimental results show that the new watermarking method is novel, secure and robust.展开更多
Medical images are a critical component of the diagnostic process for clinicians.Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the c...Medical images are a critical component of the diagnostic process for clinicians.Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images.Traditional watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient diagnosis.In addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed.This paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned problems.First,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution modules.Second,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image.At last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third party.The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark.In the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms.Our NC values for all the images are more than 90%accurate which shows the robustness of the algorithm.Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.展开更多
基金Supported by the National Natural Science Foundation of China (No.90204017, 60373059)National 973 Project (G1999035805) ISN Open Foundation
文摘All watermarks are shared into shadow images using VCS(Visual Cryptography Scheme). Only one specific shadow (as a new watermark) is embedded into the host signal and the other ones are distributed to every user in each group as a key. In the watermarking extraction procedure, users in different group can obtain different watermark by combining their shadows with the extracted one from the watermarked signal. Analysis and experimental results show that the new watermarking method is novel, secure and robust.
基金supported in part by Key Research Project of Hainan Province under Grant ZDYF2021SHFZ093the Natural Science Foundation of China under Grants 62063004 and 62162022+2 种基金the Hainan Provincial Natural Science Foundation of China under Grants 2019RC018,521QN206 and 619QN249the Major Scientific Project of Zhejiang Lab 2020ND8AD01the Scientific Research Foundation for Hainan University(No.KYQD(ZR)-21013).
文摘Medical images are a critical component of the diagnostic process for clinicians.Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images.Traditional watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient diagnosis.In addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed.This paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned problems.First,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution modules.Second,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image.At last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third party.The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark.In the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms.Our NC values for all the images are more than 90%accurate which shows the robustness of the algorithm.Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.
文摘深度神经网络(Deep Neural Network,DNN)迅速发展,知识产权保护问题成为研究热点。经典黑盒水印是在干净样本的空间域中嵌入水印来构造触发样本,此类水印方案未考虑样本的隐秘性、样本及模型的鲁棒性,且主要集中于嵌入零位水印。提出了一种基于频域算法的深度学习多用户水印方案(Frequency-Domain based deep learning Multi-User watermarking scheme,FDMU)。水印生成阶段,利用二维离散小波算法提取原数据集以外的干净样本的LL频带,再对该频带进行奇异值分解,将用户信息以0、1字符串形式嵌入到S矩阵中生成水印样本。水印嵌入阶段,利用对抗样本白盒攻击为水印样本定向生成触发样本及特定错误标签,训练模型实现水印目的。水印验证阶段,使用黑盒水印验证方式,输入多组特定触发器触发DNN特定行为,利用逆变换频率算法提取用户信息,实现模型所有权的验证。实验结果表明:使用基于频域的FDMU水印方案,在嵌入用户信息的前提下,模型具有高隐秘性、高保真性和高稳定性,能够抵御模型微调和模型剪枝攻击。
